
"""simple docstring"""


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np



class 	__a	:






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	List[str]:







              """simple docstring"""
              torch.manual_seed(0      )
              UpperCamelCase       			=		TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

              torch.manual_seed(0      )
              UpperCamelCase       			=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

              torch.manual_seed(0      )
              UpperCamelCase       			=		UNetaDConditionModel(
                  sample_size=32							,   layers_per_block=1							,   block_out_channels=[32, 64]							,   down_block_types=[
                      "ResnetDownsampleBlock2D",
                      "SimpleCrossAttnDownBlock2D",
                  ]							,   mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,   up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,   in_channels=3							,   out_channels=6							,   cross_attention_dim=32							,   encoder_hid_dim=32							,   attention_head_dim=8							,   addition_embed_type="text"							,   addition_embed_type_num_heads=2							,   cross_attention_norm="group_norm"							,   resnet_time_scale_shift="scale_shift"							,   act_fn="gelu"							,   )
              unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

              torch.manual_seed(0      )
              UpperCamelCase       			=		DDPMScheduler(
                  num_train_timesteps=1_000							,   beta_schedule="squaredcos_cap_v2"							,   beta_start=0.0001							,   beta_end=0.02							,   thresholding=UpperCAmelCase_							,   dynamic_thresholding_ratio=0.95							,   sample_max_value=1.0							,   prediction_type="epsilon"							,   variance_type="learned_range"							,   )

              torch.manual_seed(0      )
              UpperCamelCase       			=		IFWatermarker()

              return {
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "watermarker": watermarker,
                  "safety_checker": None,
                  "feature_extractor": None,
              }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[str]:







              """simple docstring"""
              torch.manual_seed(0      )
              UpperCamelCase       			=		TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

              torch.manual_seed(0      )
              UpperCamelCase       			=		AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

              torch.manual_seed(0      )
              UpperCamelCase       			=		UNetaDConditionModel(
                  sample_size=32							,   layers_per_block=[1, 2]							,   block_out_channels=[32, 64]							,   down_block_types=[
                      "ResnetDownsampleBlock2D",
                      "SimpleCrossAttnDownBlock2D",
                  ]							,   mid_block_type="UNetMidBlock2DSimpleCrossAttn"							,   up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]							,   in_channels=6							,   out_channels=6							,   cross_attention_dim=32							,   encoder_hid_dim=32							,   attention_head_dim=8							,   addition_embed_type="text"							,   addition_embed_type_num_heads=2							,   cross_attention_norm="group_norm"							,   resnet_time_scale_shift="scale_shift"							,   act_fn="gelu"							,   class_embed_type="timestep"							,   mid_block_scale_factor=1.414							,   time_embedding_act_fn="gelu"							,   time_embedding_dim=32							,   )
              unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

              torch.manual_seed(0      )
              UpperCamelCase       			=		DDPMScheduler(
                  num_train_timesteps=1_000							,   beta_schedule="squaredcos_cap_v2"							,   beta_start=0.0001							,   beta_end=0.02							,   thresholding=UpperCAmelCase_							,   dynamic_thresholding_ratio=0.95							,   sample_max_value=1.0							,   prediction_type="epsilon"							,   variance_type="learned_range"							,   )

              torch.manual_seed(0      )
              UpperCamelCase       			=		DDPMScheduler(
                  num_train_timesteps=1_000							,   beta_schedule="squaredcos_cap_v2"							,   beta_start=0.0001							,   beta_end=0.02							,   )

              torch.manual_seed(0      )
              UpperCamelCase       			=		IFWatermarker()

              return {
                  "text_encoder": text_encoder,
                  "tokenizer": tokenizer,
                  "unet": unet,
                  "scheduler": scheduler,
                  "image_noising_scheduler": image_noising_scheduler,
                  "watermarker": watermarker,
                  "safety_checker": None,
                  "feature_extractor": None,
              }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		self.get_dummy_components()
              UpperCamelCase       			=		self.pipeline_class(**UpperCAmelCase_      )
              pipe.to(UpperCAmelCase_      )
              pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

              UpperCamelCase       			=		self.get_dummy_inputs(UpperCAmelCase_      )

              UpperCamelCase       			=		inputs["prompt"]
              UpperCamelCase       			=		inputs["generator"]
              UpperCamelCase       			=		inputs["num_inference_steps"]
              UpperCamelCase       			=		inputs["output_type"]

              if "image" in inputs:
                     UpperCamelCase       			=		inputs["image"]
              else:
                     UpperCamelCase       			=		None

              if "mask_image" in inputs:
                     UpperCamelCase       			=		inputs["mask_image"]
              else:
                     UpperCamelCase       			=		None

              if "original_image" in inputs:
                     UpperCamelCase       			=		inputs["original_image"]
              else:
                     UpperCamelCase       			=		None

              UpperCamelCase			,							UpperCamelCase       			=		pipe.encode_prompt(UpperCAmelCase_      )

              # inputs with prompt converted to embeddings
              UpperCamelCase       			=		{
                  "prompt_embeds": prompt_embeds,
                  "negative_prompt_embeds": negative_prompt_embeds,
                  "generator": generator,
                  "num_inference_steps": num_inference_steps,
                  "output_type": output_type,
              }

              if image is not None:
                     UpperCamelCase       			=		image

              if mask_image is not None:
                     UpperCamelCase       			=		mask_image

              if original_image is not None:
                     UpperCamelCase       			=		original_image

              # set all optional components to None
              for optional_component in pipe._optional_components:
                     setattr(UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		pipe(**UpperCAmelCase_      )[0]

              with tempfile.TemporaryDirectory() as tmpdir:
                     pipe.save_pretrained(UpperCAmelCase_      )
                     UpperCamelCase       			=		self.pipeline_class.from_pretrained(UpperCAmelCase_      )
                     pipe_loaded.to(UpperCAmelCase_      )
                     pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_      )

              pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

              for optional_component in pipe._optional_components:
                     self.assertTrue(
                         getattr(UpperCAmelCase_							,   UpperCAmelCase_      ) is None							,   f"`{optional_component}` did not stay set to None after loading."							,   )

              UpperCamelCase       			=		self.get_dummy_inputs(UpperCAmelCase_      )

              UpperCamelCase       			=		inputs["generator"]
              UpperCamelCase       			=		inputs["num_inference_steps"]
              UpperCamelCase       			=		inputs["output_type"]

              # inputs with prompt converted to embeddings
              UpperCamelCase       			=		{
                  "prompt_embeds": prompt_embeds,
                  "negative_prompt_embeds": negative_prompt_embeds,
                  "generator": generator,
                  "num_inference_steps": num_inference_steps,
                  "output_type": output_type,
              }

              if image is not None:
                     UpperCamelCase       			=		image

              if mask_image is not None:
                     UpperCamelCase       			=		mask_image

              if original_image is not None:
                     UpperCamelCase       			=		original_image

              UpperCamelCase       			=		pipe_loaded(**UpperCAmelCase_      )[0]

              UpperCamelCase       			=		np.abs(to_np(UpperCAmelCase_      ) - to_np(UpperCAmelCase_      )      ).max()
              self.assertLess(UpperCAmelCase_							,   1e-4      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.get_dummy_components()
              UpperCamelCase       			=		self.pipeline_class(**UpperCAmelCase_      )
              pipe.to(UpperCAmelCase_      )
              pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

              UpperCamelCase       			=		self.get_dummy_inputs(UpperCAmelCase_      )
              UpperCamelCase       			=		pipe(**UpperCAmelCase_      )[0]

              with tempfile.TemporaryDirectory() as tmpdir:
                     pipe.save_pretrained(UpperCAmelCase_      )
                     UpperCamelCase       			=		self.pipeline_class.from_pretrained(UpperCAmelCase_      )
                     pipe_loaded.to(UpperCAmelCase_      )
                     pipe_loaded.set_progress_bar_config(disable=UpperCAmelCase_      )

              pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

              UpperCamelCase       			=		self.get_dummy_inputs(UpperCAmelCase_      )
              UpperCamelCase       			=		pipe_loaded(**UpperCAmelCase_      )[0]

              UpperCamelCase       			=		np.abs(to_np(UpperCAmelCase_      ) - to_np(UpperCAmelCase_      )      ).max()
              self.assertLess(UpperCAmelCase_							,   1e-4      )







"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





